add missing pieces

app.py

@@ -1,25 +1,18 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: os_identify.ipynb.
 
 # %% auto 0
-__all__ = ['btn_upload', 'btn_run', 'path', 'learn_inf', 'out_pl', 'lbl_pred', 'on_click_classify']
+__all__ = ['path', 'learn_inf', 'title', 'description', 'article', 'examples', 'interpretation', 'enable_queue', 'labels',
+           'predict']
 
 # %% os_identify.ipynb 4
 from fastai.vision.all import *
-from fastai.vision.widgets import *
+# from fastai.vision.widgets import *
 import gradio as gr
 
-# %% os_identify.ipynb 5
-def on_click_classify(change):
-    img = PILImage.create(btn_upload.data[-1])
-    out_pl.clear_output()
-    with out_pl: display(img.to_thumb(128,128))
-    pred,pred_idx,probs = learn_inf.predict(btn_upload.data[-1])
-    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
-
 # %% os_identify.ipynb 6
-btn_upload = widgets.FileUpload()
-btn_run = widgets.Button(description='Classify')
-btn_run.on_click(on_click_classify)
+# btn_upload = widgets.FileUpload()
+# btn_run = widgets.Button(description='Classify')
+# btn_run.on_click(on_click_classify)
 
 # %% os_identify.ipynb 7
 # load the model
@@ -27,11 +20,28 @@ path = Path()
 learn_inf = load_learner(path/'os_model.pkl')
 
 # %% os_identify.ipynb 8
-out_pl = widgets.Output()
-out_pl.clear_output()
-lbl_pred = widgets.Label()
+# out_pl = widgets.Output()
+# out_pl.clear_output()
+# lbl_pred = widgets.Label()
 
 
 # %% os_identify.ipynb 9
-VBox([widgets.Label('Select your screencap!'),
-      btn_upload, btn_run, out_pl, lbl_pred])
+# VBox([widgets.Label('Select your screencap!'),
+#       btn_upload, btn_run, out_pl, lbl_pred])
+
+# %% os_identify.ipynb 10
+title = "Operating System Screencap Classifier"
+description = "A classifier trained on various operating system screenshots. For better results, use screenshots that clearly show unique UI elements. For best results, help me better a better dataset."
+article="<p>article goes here</p>"
+examples=['win95.jpg']
+interpretation="default"
+enable_queue=True
+
+# %% os_identify.ipynb 11
+labels = learn_inf.dls.vocab
+def predict(img):
+    # img = PILImage.create(img)
+    pred,pred_idx,probs = learn_inf.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)
+# gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)

requirements.txt

@@ -1,2 +1,3 @@
 fastai
 gradio
+scikit-image